Spectral fingerprinting of ovarian cancer in serum samples
Ovarian cancer can be predicted with high sensitivity and specificity via a fingerprint obtained, via machine learning, from near-infrared fluorescence emissions of an array of carbon nanotube sensors in serum samples.
Published
The cover illustrates that the analysis, via machine learning, of near-infrared-fluorescence emissions of carbon-nanotube sensors placed in serum samples can be used to predict ovarian cancer.
Image: Olga Kharchenko. Cover design: Alex Wing.
